Abstract
Bipolar disorder is a psychiatric condition characterized by episodes of elevated mood interspersed with episodes of depression. While treatment developments and understanding the disruptive nature of this illness have focused on these episodes, it is also evident that some patients may have chronic week-to-week mood instability. This is also a major morbidity. The longitudinal pattern of this mood instability is poorly understood as it has, until recently, been difficult to quantify. We propose that understanding this mood variability is critical for the development of cognitive neuroscience-based treatments. In this study, we develop a time-series approach to capture mood variability in two groups of patients with bipolar disorder who appear on the basis of clinical judgement to show relatively stable or unstable illness courses. Using weekly mood scores based on a self-rated scale (quick inventory of depressive symptomatology—self-rated; QIDS-SR) from 23 patients over a 220-week period, we show that the observed mood variability is nonlinear and that the stable and unstable patient groups are described by different nonlinear time-series processes. We emphasize the necessity in combining both appropriate measures of the underlying deterministic processes (the QIDS-SR score) and noise (uncharacterized temporal variation) in understanding dynamical patterns of mood variability associated with bipolar disorder.
Highlights
Bipolar disorder is a mental disorder characterized by manic episodes of elevated mood and overactivity, interspersed with periods of depression [1]
Nonlinear time-series analysis is a well-developed field of statistical research (e.g. [34]), and we extend these methods to explore mood variability in patients diagnosed with bipolar disorder
Here, we have developed a nonlinear time-series approach and shown that we can characterize mood variability in two groups of patients with bipolar disorder using particular types of time-series models based on AR processes
Summary
Bipolar disorder is a mental disorder characterized by manic episodes of elevated mood and overactivity, interspersed with periods of depression [1]. 1 per cent of adults in the general population have a lifetime prevalence of bipolar disorder [2]. It is a major cause of morbidity and mortality owing to suicide [3], with the highest rate of suicide of all the psychiatric disorders [4]. Recovery from individual manic and depressive episodes was seen as a hallmark feature which classically distinguished even severe bipolar illness from schizophrenia. This perspective has shifted with the recognition that ongoing mood variation may lead to chronic functional impairment. Until recently it remained difficult to measure mood stability and so, despite its apparent importance, we know strikingly little about the nature of bipolar mood variation over time in patients
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